{"id":2973,"date":"2021-03-27T22:12:28","date_gmt":"2021-03-27T14:12:28","guid":{"rendered":"http:\/\/www.magicandlove.com\/blog\/?p=2973"},"modified":"2025-08-18T18:12:21","modified_gmt":"2025-08-18T10:12:21","slug":"be-a-hong-kong-patriot-part-3-the-red-scout","status":"publish","type":"post","link":"http:\/\/www.magicandlove.com\/blog\/2021\/03\/27\/be-a-hong-kong-patriot-part-3-the-red-scout\/","title":{"rendered":"Be a Hong Kong Patriot, Part 3, The Red Scout (2019)"},"content":{"rendered":"\n

\u505a\u611b\u570b\u6e2f\u4eba\u4e4b\u4e09\uff0c\u7d05\u8272\u7ae5\u5b50\u8ecd<\/h2>\n\n\n\n
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Introduction<\/h3>\n\n\n\n

Be a Hong Kong Patriot, Part 3 \u2013 The Red Scout<\/em> is a joke for the participating audience. More than a decade ago, in the Part 1 \u2013 Love Takes the Victoria Peak<\/a> of the series, The artist prepared a dildo attached with the Chinese national flag that waved according to the current Hong Kong Hang Seng Stock Index<\/a>. In the Part 2 \u2013 The Fuzzy Wanker<\/a> associated the Internet traffic of the listed companies from the local stock market with the tangible flow of small metal particles. The Part 3 of the series \u2013 The Red Scout will judge a member of the audience, based on a portrait photograph to tell if he\/she is patriotic or not.<\/p>\n\n\n\n

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Be a Hong Kong Patriot, Part 3 \u2013 The Red Scout, exhibition at the Lumenvisum<\/figcaption><\/figure>\n\n\n\n

Background<\/h3>\n\n\n\n

Similar to the Part 1 and Part 2, the Chinese title of the artwork  \u7d05\u8272\u7ae5\u5b50\u8ecd was also modeled upon the Chinese revolutionary propaganda opera \u7d05\u8272\u5a18\u5b50\u8ecd, The Red Detachment of Women, 1962. Nevertheless, the content of the opera did not have any relationship with this project.<\/p>\n\n\n\n

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The Red Detachment of Women, 1962 (image from Wikipedia)<\/figcaption><\/figure>\n\n\n\n

Information about artificial intelligence (AI) and face recognition has ever growing popularity, especially that related to the surveillance applications in China. Academic journals also cover articles on the use of AI and face recognition to predict the political stance and voting preference.<\/p>\n\n\n\n

Deep neural networks are more accurate than humans at detecting sexual orientation from facial images<\/em>. Michal W. Kosinski, Yilun Wang, Journal of Personality and Social Psychology, February 2018, Vol. 114, Issue 2, Pages 246-257.<\/a><\/p>\n\n\n\n

Political ideology at face value<\/em>. Jakub Samochowiec, Michaela W\u00e4nke, Klaus Fiedler, Social Psychology and Personality Science, first publishedJuly 19, 2010.<\/a><\/p>\n\n\n\n

Democrats and Republicans can be differentiated from their faces<\/em>. Nicholas O. Rule, Nalini Ambady, Public Library of Science, first published Jan 18, 2010.<\/a><\/p>\n\n\n\n

In the project, a custom software was developed to classify human face and determine if the owner is patriotic or not by training an artificial neural network with hundreds of known faces of Hong Kong government officials, councilors, and political celebrities. Nevertheless there are two known issues:<\/p>\n\n\n\n

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  1. The size of the training sets is relatively small (around 300 portrait photos).<\/li>\n\n\n\n
  2. Since the application is a typical supervised learning, who is going to label the portrait photos for training?<\/li>\n<\/ol>\n\n\n\n

    As a result, the project will not be a piece of artwork working with machine learning. It will be an artwork about machine learning, and about the assumptions and limitations of machine learning in general. Eventually, the artist took up the role to label all the photos based on the public opinions and political stances of the photo owners that were available in the public domain. The key criterion was whether they demonstrated blind loyalty to the Chinese government.<\/p>\n\n\n\n

    Experiments<\/h3>\n\n\n\n

    Here are the samples of the portrait photos for training.<\/p>\n\n\n\n

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    Training data set<\/figcaption><\/figure>\n\n\n\n

    The first experiment with the data set was an unsupervised clustering into 3 groups.<\/p>\n\n\n\n

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    Result of unsupervised clustering<\/figcaption><\/figure>\n\n\n\n

    The clustering is done by the facial landmarks of each photo. The software employed the Python binding of the dlib library<\/a> to identify the facial landmarks and used scikit-learn<\/a> to perform the clustering. The following video demonstrates the extraction of facial landmarks from each of the photo in the data set.<\/p>\n\n\n\n

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